def train(trainloader,valloader,testloader,optimizers,model,epoch=10):
total_loss,total_acc = [],[]
for epoch_ in range(epoch):
for step,(x,y) in enumerate(trainloader):
with tf.GradientTape() as tape:
# [b, 32, 32, 3] => [b, 10],前向传播
logits = model(x)
y_one_hot=tf.one_hot(y,depth=100)# [b]=>[b,100]
#print('logits:',logits.shape,'y_onehot:',y_one_hot.shape)
loss=tf.losses.categorical_crossentropy(y_one_hot,logits,from_logits=True) #loss
loss=tf.reduce_mean(loss) #mean loss
grads=tape.gradient(loss,model.trainable_variables)
optimizers.apply_gradients(zip(grads,model.trainable_variables))
if step%100==0:
total_loss.append(loss)
print('epoch {}/{}'.format(epoch_,epoch),'------step:',step,'-----loss:',float(loss))
acc,total=0,0
for x, y in testloader:
acc,total=0,0
logits = model(x)
prob = tf.nn.softmax(logits, axis=1)
y_pred = tf.argmax(prob, axis=1)
y_pred = tf.cast(y_pred, dtype=tf.int32)
currect = tf.equal(y_pred, y)
acc += tf.reduce_sum(tf.cast(currect, dtype=tf.int32)).numpy()
total += x.shape[0]
print('evaluate acc', acc / total)
total_acc.append(acc)
def train_step(image_data, target):
with tf.GradientTape() as tape:
pred_result = model(image_data, training=True)
giou_loss=conf_loss=prob_loss=0
# optimizing process
for i in range(3):
conv, pred = pred_result[i*2], pred_result[i*2+1]
loss_items = compute_loss(pred, conv, *target[i], i)
giou_loss += loss_items[0]
conf_loss += loss_items[1]
prob_loss += loss_items[2]
total_loss = giou_loss + conf_loss + prob_loss
gradients = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
tf.print("=> STEP %4d lr: %.6f giou_loss: %4.2f conf_loss: %4.2f "
"prob_loss: %4.2f total_loss: %4.2f" %(global_steps, optimizer.lr.numpy(),
giou_loss, conf_loss,
prob_loss, total_loss))
# update learning rate
global_steps.assign_add(1)
if global_steps < warmup_steps:
lr = global_steps / warmup_steps *cfg.TRAIN.LR_INIT
else:
lr = cfg.TRAIN.LR_END + 0.5 * (cfg.TRAIN.LR_INIT - cfg.TRAIN.LR_END) * (
(1 + tf.cos((global_steps - warmup_steps) / (total_steps - warmup_steps) * np.pi))
)
tf.keras.optimizer.lr.assign(lr.numpy())
def train_step(x_train):
with tf.GradientTape() as tape:
probabilities = model(x_train, training=True)
loss = evaluate(probabilities)
gradients_of_generator = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(gradients_of_generator, model.trainable_variables))
return loss
def train_epoch(epoch):
for step, (x, y) in enumerate(train_dataset):
with tf.GradientTape() as tape:
x = tf.reshape(x, (-1, 28 * 28))
out = model(x)
loss = tf.reduce_sum(tf.square(out - y) / x.shape[0])
grads = tape.gradient(loss, model.trainable_variables)
optimizers.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, 'loss', loss.numpy())
def main():
for epoch in range(30):
for step, (x, y) in enumerate(db):
x = tf.reshape(x, [-1, 28 * 28])
with tf.GradientTape() as tape:
logits = model(x)
y_onehot = tf.one_hot(y, depth=10)
#[b]
loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits))
loss_ce = tf.losses.categorical_crossentropy(y_onehot,
logits,
from_logits=True)
loss_ce = tf.reduce_mean(loss_ce)
grads = tape.gradient(loss_ce, model.trainable_variables)
optimizers.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, 'losses:', float(loss_ce), float(loss_mse))
# test
total_correct = 0
total_num = 0
for x, y in db_test:
# x: [b, 784]
# y:[10]
x = tf.reshape(x, [-1, 28 * 28])
# [b, 10]
logits = model(x)
# logits => prob
prob = tf.nn.softmax(logits, axis=1)
# [b,10] => [b]
pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32)
# pred:[b]
# y:[b]
# print(y.shape)
# print(pred.shape)
# exit()
correct = tf.equal(pred, y)
correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32))
total_correct += int(correct)
total_num += x.shape[0]
acc = total_correct / total_num
print(epoch, 'acc:', acc)
def main():
for epoch in range(30):
for step, (x, y) in enumerate(db):
# x:[b,28,28]
# y:[b]
x = tf.reshape(x, [-1, 28 * 28])
# 利用梯度对网络进行更新
with tf.GradientTape() as tape:
# [b,784]=>[b,10], 直接调用，实现网络的传播
logits = model(x)
# onehot编码
y_onehot = tf.one_hot(y, depth=10)
loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits))
loss_ce = tf.reduce_mean(
tf.losses.categorical_crossentropy(y_onehot,
logits,
from_logits=True))
# 利用构建好的优化器直接对数据进行优化(使用交叉熵进行发现传播)
grads = tape.gradient(loss_ce, model.trainable_variables)
optimizers.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, 'loss', float(loss_ce), float(loss_mse))
# test,做前向传播，检查正确率
total_correct = 0
total_num = 0
for x, y in db_test:
# x:[b,28,28]
# y:[b]
x = tf.reshape(x, [-1, 28 * 28])
# 不需要做梯度下降，所以不需要被包围
# [b,784]=>[b,10]
logits = model(x)
# logits=>概率,[b,10]
prob = tf.nn.softmax(logits, axis=1)
# [b,10] => [b]
pred = tf.cast(tf.argmax(prob, axis=1), dtype=tf.int32)
# pred:[b];y:[b]
correct = tf.equal(pred, y)
correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32))
total_correct += int(correct)
total_num += x.shape[0]
acc = total_correct / total_num
print(epoch, 'test acc:', acc)
def main():
for epoch in range(30):
for step, (x, y) in enumerate(db):
# x:[b,28,28]
# y:[b]
x = tf.reshape(x, [-1, 28 * 28])
# 利用梯度对网络进行更新
with tf.GradientTape() as tape:
# [b,784]=>[b,10], 直接调用，实现网络的传播
logits = model(x)
# onehot编码
y_onehot = tf.one_hot(y, depth=10)
loss = tf.reduce_mean(
tf.losses.categorical_crossentropy(y_onehot,
logits,
from_logits=True))
# 2.更新loss值
loss_meter.update_state(loss)
# 利用构建好的优化器直接对数据进行优化(使用交叉熵进行发现传播)
grads = tape.gradient(loss, model.trainable_variables)
optimizers.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
# 3.打印出来现在想要的loss
print(step, 'loss: ', loss_meter.result().numpy())
# 4.清除掉之前保存的准确率数据
loss_meter.reset_states()
# test,做前向传播，检查正确率
# 调用之前要清空缓存
acc_meter.reset_states()
for x, y in db_test:
# x:[b,28,28]
# y:[b]
x = tf.reshape(x, [-1, 28 * 28])
# 不需要做梯度下降，所以不需要被包围
# [b,784]=>[b,10]
logits = model(x)
# logits=>概率,[b,10]
prob = tf.nn.softmax(logits, axis=1)
# [b,10] => [b]
pred = tf.cast(tf.argmax(prob, axis=1), dtype=tf.int32)
# pred:[b];y:[b]
acc_meter.update_state(y, pred)
print(epoch, 'test acc:', acc_meter.result().numpy())
def main():
for epoch in range(30):
for step, (x_, y_) in enumerate(db):
# x_: [b, 28*28] -> [b, 784]
# y_: [b]
x_ = tf.reshape(x_, [-1, 28 * 28])
with tf.GradientTape() as tape:
# [b, 784] -> [b, 10]
logits = model(x_)
y_onehot = tf.one_hot(y_, depth=10)
loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits))
loss_cs = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True))
grads = tape.gradient(loss_cs, model.trainable_variables)
optimizers.apply_gradients(grads_and_vars=zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, 'loss: ', float(loss_mse), float(loss_cs))
# test
total_correct = 0
total_num = 0
for x, y in db_test:
# x: [b, 28, 28] => [b, 784]
# y: [b]
x = tf.reshape(x, [-1, 28 * 28])
# [b, 10]
logits = model(x)
# logits => prob, [b, 10]
prob = tf.nn.softmax(logits, axis=1)
# [b, 10] => [b], int64
pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32)
# pred:[b]
# y: [b]
# correct: [b], True: equal, False: not equal
correct = tf.equal(pred, y)
correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32))
total_correct += int(correct)
total_num += x.shape[0]
acc = total_correct / total_num
print(epoch, 'test acc:', acc)
xs = tf.convert_to_tensor(xs, dtype=tf.float32) / 255.
db = tf.data.Dataset.from_tensor_slices((xs, ys))
db = db.batch(batch_size).repeat(30)
model = Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(10)
])
model.build(input_shape=(4, 28 * 28))
model.summary()
optimizers = optimizers.SGD(lr=0.01)
acc_meter = metrics.Accuracy()
for step, (x, y) in enumerate(db):
with tf.GradientTape() as tap:
x = tf.reshape(x, (-1, 28 * 28))
out = model(x)
y_onehot = tf.one_hot(y, depth=10)
loss = tf.square(out - y_onehot)
loss = tf.reduce_sum(loss) / x.shape[0]
acc_meter.update_state(tf.argmax(out, axis=1), y)
grads = tap.gradient(loss, model.trainable_variables)
optimizers.apply_gradients(zip(grads, model.trainable_variables))
if step % 200 == 0:
print(step, 'loss', float(loss), 'acc:', acc_meter.result().numpy())
acc_meter.reset_states()
# %%
# 构建梯度记录环境
with tf.GradientTape() as tape:
# 插入通道维度，=>[b,28,28,1]
x = tf.expand_dims(x, axis=3)
# 前向计算，获得10类别的预测分布，[b, 784] => [b, 10]
out = network(x)
# 真实标签one-hot编码，[b] => [b, 10]
y_onehot = tf.one_hot(y, depth=10)
# 计算交叉熵损失函数，标量
loss = criteon(y_onehot, out)
# 自动计算梯度
grads = tape.gradient(loss, network.trainable_variables)
# 自动更新参数
optimizers.apply_gradients(zip(grads, network.trainable_variables))
# %%
# 记录预测正确的数量，总样本数量
correct, total = 0, 0
for x, y in db_test: # 遍历所有训练集样本
# 插入通道维度，=>[b,28,28,1]
x = tf.expand_dims(x, axis=3)
# 前向计算，获得10类别的预测分布，[b, 784] => [b, 10]
out = network(x)
# 真实的流程时先经过softmax，再argmax
# 但是由于softmax不改变元素的大小相对关系，故省去
pred = tf.argmax(out, axis=-1)
y = tf.cast(y, tf.int64)
# 统计预测正确数量
correct += float(tf.reduce_sum(tf.cast(tf.equal(pred, y), tf.float32)))
